Method for generating a block-based image histogram

ABSTRACT

A method for generating a block-based image histogram from data compressed by JPEG, MPEG-1, and MPEG-2, or uncompressed image data employing block-based linear quantization to generate histograms that include color, brightness, and edge components. The edge histogram, in particular, includes the global edge features, semi-global edge features, and local edge features. The global edge histogram is based on image blocks of the entire image space. The local edge histogram is based on a group of edge blocks. The semi-global edge histogram is based on the horizontally and the vertically grouped image blocks. A method for generating block-based image histogram with color information and brightness information of image data in accordance with an embodiment of the present invention extracts feature information of an image in terms of the block and updates global histogram bins on the basis of the feature information. The method for generating block-based image histogram with color information and brightness information of image data minimizes quantization error by employing linear weight and updates values of histogram bins. The error that occurs at a boundary between bins of the histograms and the linear weight depends on the distance between the histogram bins.

BACKGROUND OF THE INVENTION

1. Field of the Invention The present invention relates to a method forgenerating a block-based image histogram from data compressed by JPEG,MPEG-1, and MPEG-2, or uncompressed image data. In particular, themethod employs block-based linear quantization to generate histogramsthat include color, brightness, and edge components.

2. Description of the Related Art

JPEG is the international standard for still images and MPEG-1, 2 arefor moving pictures. Regarding the compressed image information, featureinformation is necessary for applications such as extracting key frames,searching images, and browsing.

To extract such feature information, a brightness and color histogramthat express relative frequency of brightness and color (red, green,blue) in an image is widely used. Methods comparing histograms have beenproposed for searching digital video applications. As histograms areused for searching images and detecting motion change, it is proposedthat conventional histograms be improved. That is, conventional singlecomponent histograms with discrete quantization and color have beendeveloped, and therefore composite histograms that employ linear updateand soft decision are adopted for effective and efficient imagedescription.

U.S. Pat. No. 5,805,733 “Method and system for detecting scenes andsummarizing video sequence” disclosed a method that employs colorhistograms and edge maps for detecting motion change. Though the methodis effective in that it extracts color information in consideration ofthe human eye, it doesn't use brightness information. A method disclosedby a technical paper “Color Indexing” published by International Journalof Computer Vision receives color information and measures similarity ofimages by histogram intersection method. However, this method doesn'tuse brightness information and therefore accuracy is not good enough.Also, since the conventional methods generate histograms using adiscrete quantization method, a relatively large number of histogrambins are needed to achieve good performance. Consequentially, thesemethods are not efficient in terms of storage and similaritymeasurement.

In addition, because the conventional methods perform feature extractionin terms of pixel in generating histograms, feature information is veryrestrictively generated.

BRIEF SUMMARY OF THE INVENTION

A method for generating a block-based image histogram is provided.

A method for generating a block-based image histogram using a colorinformation and a brightness information of an image data includes thefollowing steps. The first step is to extract edge information of animage in terms of a block. The second step is to update edge histogrambins on the basis of the edge information to generate a global edgehistogram.

Preferably, the step of extracting the edge information furthercomprises the following steps. The first step is to divide the blockinto four sub-blocks by dividing the block by half with respect tohorizontal direction and dividing the block by half with respect tovertical direction. The second step is to obtain brightnessrepresentative values of the sub-blocks, respectively. The third step isto determine if an edge exists in the block and determining an edge typeby comparing a brightness difference between the adjacent sub-blockswith a threshold.

Preferably, the determining step determines that the edge exists anddetects the edge type if the brightness difference is larger than thethreshold or determines that the block is a monotone block if thebrightness difference is not larger than the threshold.

Preferably, the brightness representative value of the sub-block is anaverage value of the pixels within the sub-block.

Preferably, the brightness representative value of the sub-block is amedian value of the pixels within the sub-block.

Preferably, another method of extracting the edge information comprisesthe following steps. The first step is to divide the block into foursub-blocks by dividing the block by half with respect to a horizontaldirection and dividing the block by half with respect to a verticaldirection. The second step is to obtain brightness representative valuesof the sub-blocks, respectively. The third step is to obtain an edgevalue of 0 degree, an edge value of 45 degree, an edge value of 90degree, an edge value of 135 degree, and a complex edge value byconvoluting the brightness representative values with filtercoefficients. The fourth step is to determine if an edge exists in theblock and determining an edge type by comparing a maximum edge valuewith a threshold, wherein the maximum edge value is the largest valueamong the edge value of 0 degrees, the edge value of 45 degrees, theedge value of 90 degrees, the edge value of 135 degrees, and a complexedge value.

Preferably, the obtaining step of the edge value of 0 degrees, the edgevalue of 45 degrees, the edge value of 90 degrees, the edge value of 135degrees, and the complex edge value is calculated by applying thebrightness representative values and the filter coefficients tofollowing equations.${{edge}\quad 90} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {edge}\quad 90{\_ filter}(i)} \right)}}$${{edge}\quad 0} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {edge}\quad 0{\_ filter}(i)} \right)}}$${{edge}\quad 45} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {edge}\quad 45{\_ filter}(i)} \right)}}$${{edge}\quad 135} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {edge}\quad 135{\_ filter}(i)} \right)}}$${complex\_ edge} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {complex\_ edge}{\_ filter}(i)} \right)}}$

wherein the edge90_filter(i), the edge45_filter(i), theedge135_filter(i), and the complex_edge_filter(i) are filtercoefficients, and wherein the mean_sub_block(i) is average brightnessvalue of ith sub-block.

Preferably, the determination step determines the maximum edge value asa representative edge of the block if the maximum edge value is largerthan the threshold or determines the block as a monotone block if themaximum edge value is not larger than the threshold.

Preferably, the method for generating a block-based image histogram inaccordance with an embodiment of the present invention further comprisesthe following step of grouping the blocks and thereby generating a localedge histogram using the edge information extracted from each block interms of the local regions.

Preferably, the method for generating a block-based image histogram inaccordance with an embodiment of the present invention further comprisesthe following step of grouping the local regions with respect tohorizontal direction and vertical direction and thereby generating asemi-global edge histogram using the edge information extracted fromeach block in terms of the semi-global regions.

Preferably, the global histogram, semi-global histogram, and the localhistogram are normalized separately.

Preferably, the method includes the following steps. The first step isto initialize a variable k and determining a block size of level k bygrouping the pixels of the image. The second step is to update relatedhistogram bins by extracting a feature information of the image in termsof a block regarding all blocks of the level k. The Third step is toupdate related histogram bins by grouping blocks of level k to form ablock of level k+1, merge the feature information of the level k, andextract the feature information of the image in terms of a blockregarding all blocks of the level k+1.

Preferably, the method for generating a block-based image histogramusing-color information and brightness information of image datacomprises the following steps. The first step is to extract the colorinformation and the brightness information by employing a linear weightdepending on a distance between histogram bins. The second step is toupdate values of the histogram bins to minimize a quantization erroroccurring at a boundary between the histogram bins.

Preferably, the step of extracting the color information and thebrightness information further comprise the following steps. The firststep is to express a hue and a saturation of the color from the givencolor space. The second step is to obtain the linear weight between thesaturated representative colors and the linear weight betweenunsaturated color and saturated color using the hue and the saturationof the color. The third step is to calculate an increase of colorhistogram bins with the linear weight and updating related colorhistograms if color of the block is pure color or calculating anincrease of color histogram bins and brightness histogram bins with thelinear weight and linear weight and updating related color histogramsand related brightness histograms if the color of the block is a darksaturated color.

Preferably, if the color space is YCbCr color space, from the Cb, Crcomponents, hue and saturation values are obtained by equation 1 andequation 2, respectively. $\begin{matrix}{{ph} = {\tan^{- 1}\frac{Cb}{Cr}}} & \left\lbrack {{Equation}\quad 1} \right\rbrack \\{{ps} = \sqrt{{Cr}^{2} + {Cb}^{2}}} & \left\lbrack {{Equation}\quad 2} \right\rbrack\end{matrix}$

Preferably, the saturation determines if the color of the block is apure saturated color or a dark unsaturated color.

Preferably, the sum of increases of color histogram bins in terms of ablock normalizes to 1.

A method for generating a block-based image histogram using colorinformation and brightness information of image data in accordance withan embodiment of the present invention includes following steps. Thefirst step is to group a plurality of pixels of the image into a block.The second step is to divide the block into four sub-blocks by dividingthe block by half with respect to a horizontal direction and dividingthe block by half with respect to a vertical direction and thenobtaining brightness representative values of the sub-blocks anddetermining if an edge exists in the block and an edge type by comparinga brightness difference between the adjacent sub-blocks with athreshold. The third step is to increase related edge histogram bins onthe basis of the edge type and thereby updating the edge histograms. Thefourth step is to obtain a hue and a saturation of the color from acolor space of the image and to obtain a linear weight betweenrepresentative saturated colors and a linear weight between thesaturated color and a unsaturated color. The fifth step is to calculatean increase of color histogram bins with the linear weight and updatingrelated color histograms if the color-of the block is a pure color orcalculating an increase of color histogram bins and brightness histogrambins with the linear weight and the linear weight and updating relatedcolor histograms and related brightness histograms if color of the blockis a dark unsaturated color.

Preferably, the method describes at least two features of information byone composite histogram in a group of the feature information, whereinthe feature information includes the color information and thebrightness information of the image.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The embodiments of the present invention will be explained withreference to the accompanying drawings, in which:

FIG. 1 is a flow diagram illustrating the method for generatingblock-based image histogram in accordance-with an embodiment of thepresent invention;

FIG. 2 is a block diagram illustrating the method for updatingblock-based image histogram bin in accordance with an embodiment of thepresent invention;

FIGS. 3A-3D show brightness representative values of sub-blocks and edgetypes, the sub-blocks being included in a block;

FIGS. 4A-4E show block filters for edge detection;

FIG. 5 is a flow diagram illustrating edge detection process;

FIG. 6 is a diagram illustrating a process to generate an upper levelblock by merging a number of lower level blocks;

FIG. 7 is a diagram illustrating an embodiment of histogram semantic;

FIGS. 8A-8D show a conventional method for updating histogram withoutconsidering linear weights;

FIGS. 9A-9D show a method for updating histogram with linear weights inaccordance with an embodiment of the present invention;

FIGS. 10A-10B show a method with linear weights between bins ofsaturated color and linear weights between saturated color andunsaturated color;

FIGS. 11A-11C show a composite histogram of a global region histogram, asemi-global region histogram, and a local region histogram; and

FIG. 12 is a diagram illustrating an embodiment of the compositehistogram shown in FIG. 11.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a flow diagram illustrating the method for generating ablock-based image histogram in accordance with an embodiment of thepresent invention.

As shown in FIG. 1, first of all, variable k is initialized. Then,pixels of the image are grouped into a group, and a size of the basicblock at level 0 is set up at step S101. Feature information isextracted from all the basic blocks at level 0 and histograms areupdated accordingly. That is, feature information is extracted at stepS102 and a histogram is updated at step S103. The update and theextraction are applied to all blocks at step S104 and step S105. At themoment, global histogram, local histogram, and semi-global histogram aregenerated at level 0. The feature information includes brightness,color, and edge information, and the extraction process is describedlater.

After feature information regarding all blocks at level 0 is extracted,variable k is increased by 1 at step S106. Feature information of level1 is generated by merging feature information of level 0 at step S107and related histograms are updated at step S108 accordingly. A block atlevel 1 may be generated by grouping blocks of level 0, and featureinformation of level 1 may be generated by merging feature informationof level 0. A process in which feature information is extracted from allblocks of level 0 and related histograms are updated is repeated at stepS109 and step S110.

If feature information generation is not completed regarding all blocksof level 0 at step S11, the variable that represents level is increasedby one at step s106. Once feature information about all levels isgenerated, the process is substantially completed.

The upper levels are useful when users want to extract featureinformation in detail and the global histogram is generated at an upperlevel.

The method for generating block-based image histogram in accordance withan embodiment of the present invention has the following five features.First, a number of pixels are grouped into a block and the block is usedas a basic unit in extracting feature information. Second, the featureinformation that is extracted in terms of block includes edge andtexture information. Third, block size is variable on the basis of imagesize and feature information of fixed quantity is available regardlessof image size. Fourth, linear weights-are-applied-in calculating anincrease in order to minimize quantization error. Fifth, regarding animage, separate histograms are generated for global region, semi-globalregion, and local region. The method in accordance with an embodiment ofthe present invention is explained with emphasis on the five features.

In conventional methods for generating histograms, brightness featureinformation or color feature information that are extracted from pixelsof an image is updated at only related histogram bins. However, in anembodiment of the present invention, a number of pixels are grouped intoa block and the related histogram bins are updated in terms of theblock. That is, an update of histogram bins is defined in block unit andtherefore not only brightness and color features of an image but alsoedge feature information that has various resolutions is extracted. Inaddition, block size may be changed on the basis of image size andtherefore feature information of fixed quantity may be extractedregardless of image size.

When the image is a still image compressed by JPEG, 16×16 pixels size ofthe macro block may be used as a unit block. When the image iscompressed by MPEG, four DCT (Discrete Cosine Transform) blocks may beused as a unit block. Likewise, a large unit block size may be used inproportion to a large image size.

FIG. 2 is a block diagram illustrating the method for updatingblock-based image histogram bin in accordance with an embodiment of thepresent invention. Feature information extracted from each block is usedto update histogram bins.

Feature information that is extracted from a block is explained.

In the method in accordance with an embodiment of the present invention,pixels are grouped into a block and feature information is extractedfrom the block. Therefore, not only brightness information and colorinformation, but also other feature information such as edge informationgenerated in the block and texture information may be acquired. Thefeature information may be reflected for updating a composite histogram.The composite histogram represents various types of histogram bins by ahistogram. As shown in FIGS. 3A-3D, a block includes four sub-blocks.Each sub-block contains a number of pixels and the sub-block isrepresented by an average value or median value of the number of pixels.Edges of the sub-blocks may be detected in the following manner.

The first embodiment of the method for detecting edges is as follows.

As shown in FIG. 3A, a block includes four sub-blocks. Brightnessrepresentative value of each sub-block is named as DC1, DC2, DC3, andDC4. A threshold value, Te, is defined for detecting edges betweenblocks. If the brightness difference between blocks is larger than Te,an edge exists and the edge is described by a thick sold line. As shownin FIG. 3B, FIG. 3C, and FIG. 3D, the edge may be categorized into oneof three kinds. FIG. 3B shows a vertical edge. FIG. 3C shows ahorizontal edge. FIG. 3D shows a miscellaneous edge. If an edge isdetected, the- related histogram bin is increased by one.

The second embodiment of the method for detecting edges is as follows.

FIGS. 4A-4E show block filters for edge detection. FIG. 4A showscoefficient values of edge 90. FIG. 4B shows coefficient values of edge0. FIG. 4C shows coefficient values of edge 45. FIG. 4D showscoefficient values of edge 135. FIG. 4E shows coefficient values ofcomplex_edge. FIG. 5 is a flow diagram illustrating edge detectionprocess.

As shown in FIG. 5, an edge value of 0 degrees (edge0), an edge value of45 degrees (edge45), an edge value of 90 degrees (edge90), an edge valueof 135 degrees (edge135), and a complex edge value (complex_edge) areobtained at step S501. The edge values may be obtained by convolutingfive filters shown in FIG. 4 and representative values of sub-blocks.This is described in equation 1. $\begin{matrix}{{{{edge}\quad 90} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {edge}\quad 90{\_ filter}(i)} \right)}}}{{{edge}\quad 0} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {edge}\quad 0{\_ filter}(i)} \right)}}}{{{edge}\quad 45} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {edge}\quad 45{\_ filter}(i)} \right)}}}{{{edge}\quad 135} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {edge}\quad 135{\_ filter}(i)} \right)}}}{{complex\_ edge} = {{\sum\limits_{i = 0}^{3}\left( {{mean\_ sub}{\_ block}(i) \times {complex\_ edge}{\_ filter}(i)} \right)}}}} & \left\lbrack {{Equation}\quad 1} \right\rbrack\end{matrix}$

In equation 1, mean_sub_block(0), mean_sub_block(1), mean_sub_block (2),and mean_sub_block(3) represent DC1 of FIG., DC2, DC3, and DC4respectively. Also, edge90_filter(i), edge45_filter(i),edge135_filter(i), and complex_edge_filter(i) represent filtercoefficient values shown in FIG. 4. For example, edge45_filter(0) andedge45_filter(3) are √{square root over (2)} and edge45_filter(1) andedge45_filter(2) are 0.

After the edge value of 0 degrees (edge0), the edge value of 45 degrees(edge45), the edge value of 90 degrees (edge90), the edge value of 135degrees (edge135), and the complex edge value (complex_(‘)edge) arecalculated, it is determined if the largest value among those edgevalues is larger than the threshold (Th_sub_difference) at step S502.Then a monotone block and an edge block are discriminated. That is, ifthe maximum edge value is larger than the threshold value, thecorresponding edge is determined as the representative edge of the blockat step S503. However, if the maximum edge value is smaller than thethreshold, the block is determined as a monotone block at step S504.After edge types are determined, the value of the related histogram binis increased by one.

The variable size-block of feature information-extracted from blocks-isexplained.

So far, distribution of brightness, color, and edge of an image has beenextracted in a certain resolution. However, feature information needs tobe extracted by various resolutions in order to describe the imageefficiently. In an embodiment of the present invention, as shown in FIG.6, upper level (level 1) is defined by grouping a number of basic blocks(level 0). That is, a number of blocks at lower level k define a blockat upper level k+1. In FIG. 6, blocks from (0,0) to (5,5) are blocks atlevel k. Nine blocks of level k are grouped and blocks between (0,0) and(2,2) become B00 block at level k+1. Blocks between (0,3) and (2,5)become B01 block at level k+1. Blocks between (3,0) and (5,2) become B10block at level k+1. Blocks between (3,3) and (5,5) become B11 block atlevel k+1.

Edge components are extracted from each level and histogram bins aregenerated to describe images efficiently. Feature information of upperlevel block may be simply obtained from feature information of upperlevel blocks. Since feature information of upper level block containsinformation regarding position information in an image, histograms ofthe semantic that contains feature information of position andresolution regarding upper level blocks and lower level blocks may beformed as shown in FIG. 7. That is, vertical edge histogram bins,horizontal edge histogram bins, and miscellaneous edge histogram binsmay be generated separately.

Linear update is now explained.

As shown in FIGS. 8A-8D, regarding colors, conventional methods forupdating histograms employ a binary decision that increases relatedhistogram bins on the basis of existence of data in given region. Inthese cases, if some data exists at a boundary between bins,similar-color features are reflected, yet the features are allocated tothe two bins separately. Therefore, they are described as differentfeature information and it causes a serious error. To cancel out theerror, low-pass filters have been employed for smoothing histograms inconventional methods. However, since the smoothing operation causes losson feature information, precise feature information of the image cannotbe reflected.

FIGS. 8A-8D show a conventional method for updating a histogram withoutconsidering linear weights.

Even though the “A” part of FIG. 8A and FIG. 8C all have orange, FIG. 8Ahas an orange color that is close to a red color, and FIG. 8C has anorange color that is close to a yellow color. Though these two colorsare all counted as an orange color, the orange color of FIG. 8A iscounted to the red histogram bin shown in FIG. 8B, yet the FIG. 8Corange color is counted to the yellow histogram bin shown in FIG. 8D.Like this, information exists between bins having similar features butthey are assigned to different bins and therefore they are described asdifferent feature information in conventional methods, which may causesignificant problems.

To solve such problems, linear weights are applied in accordance withthe distance between bins in an embodiment of the present invention andtherefore a quantization error caused between bins is minimized.

FIG. 9 shows a method for updating histograms with linear weights inaccordance with an embodiment of the present invention.

As shown in FIGS. 9A-9D, the “A” part of FIG. 9A and FIG. 9C all have anorange color and their positions are in a boundary between red andyellow. More weights are assigned to the red color for an orange colorthat is close to the red color and the histogram is counted accordinglyas shown in FIG. 9B. More weights are assigned to a yellow color for theorange color that is close to a yellow color and the histogram iscounted accordingly as shown in FIG. 9D. Linear weights are applied inproportion to the distance between bins and therefore quantizationerrors caused in between bins are minimized. Therefore, it isadvantageous that even histograms with a small number of bins are ableto reflect feature information precisely.

An embodiment of the linear update functions as follows.

FIG. 10 shows an embodiment in which magenta, blue, cyan, green, andyellow are prepared as representative hues. Unsaturated color isseparated into five levels on the basis of brightness and histogram binsare counted. In this case, histogram bins h(0) through h(5) mean colorhistograms and histogram bins h(6) through h(10) mean brightnesshistograms.

In embodiments of the present invention, pseudo hue and pseudosaturation are calculated and used in a YcbCr color space. However, if acolor space requires hue and saturation, for example, HSV color space isemployed, hue and saturation are used instead of pseudo hue and pseudosaturation.

In the YcbCr color space, pseudo hue and pseudo saturation are definedas follows. $\begin{matrix}{{{{{Pseudo}\quad{Hue}\text{:}\quad{ph}} = {\tan^{- 1}\frac{Cb}{Cr}}}{{{Pseudo}\quad{Saturation}\quad{ps}} = \sqrt{{Cr}^{2} + {Cb}^{2}}}}\quad} & \left\lbrack {{Equation}\quad 2} \right\rbrack\end{matrix}$

The linear weight (α) between representative hues of saturated colorregion and the linear weight (β) between saturated color and unsaturatedcolor are defined as follows. $\begin{matrix}{{\alpha = {\frac{{{ph}\left( {p,q} \right)} - {ph}_{i}}{{ph}_{i + 1} - {ph}_{i}} = \frac{\theta}{60}}},{\beta = \frac{{ps}\left( {p,q} \right)}{T_{s}}}} & \left\lbrack {{Equation}\quad 3} \right\rbrack\end{matrix}$

-   ph_(i): ith representative hue-   ph(p,q): hue at color space (p,q), that is, angle at color space-   ps(p,q): saturation at color space (p,q), that is, distance from the    origin of color space

Color information with hue information is only used as color featureinformation that is not related to the brightness of the image. Aroundthe origin of color space, Cr and Cb have values about zero, which meansunsaturated color. Generally, this hue value and the distance from theorigin of color space are used as a threshold to separate saturatedcolor and unsaturated color. When natural images are employed, manycases are around the threshold and therefore serious errors are caused.That is, relatively bright colors are updated at saturated colorhistogram bins and relatively dark colors are updated at unsaturatedcolor histogram bins. Likewise, two colors of similar brightness areupdated at different color bins.

In an embodiment of the present invention, an update method with linearweights is employed for the boundary between unsaturated color andsaturated color.

Let's suppose that a starting threshold of unsaturated color is Ts.Then, a color whose saturation is larger than Ts is a pure color. Asshown in FIG. 10A, each bin values (h(i)) of color histograms isincreased by applying only a linear weight (α). The amount of increaseis obtained by the following equation.h(i)=h(i)+(1.0−α), h(i+1)=h(i+1)+α   [Equation 4]where i=0,1,2,3,4,5

If saturation (ps) is not larger than Ts and the color is close to theorigin in color space, linear weight α and linear weight β are used toincrease bins of color histograms as shown in FIG. 10B. The amount ofincrease is obtained by following equation.h(i)=h(i)+(1.0−α)×β, h(i+1)=h(i+1)+α×β   [Equation 5]where i=0,1,2,3,4,5

In addition, in the case of colors whose brightness is dark, abrightness histogram has five bins and the brightness histogram bins areincreased by following equation with linear weight β.IF (0≦luminance value of unsaturated color region<52) THENh(6)=h(6)+(1−β)ELSEIF (52≦luminance value of unsaturated color region<103) THENh(7)=h(7)+(1−β)ELSEIF (103≦luminance value of unsaturated color region<154) THENh(8)=h(8)+(1−β)ELSEIF (154≦luminance value of unsaturated color region<204) THENh(9)=h(9)+(1−β)ELSEIF (204≦luminance value of unsaturated color region<256) THENh(10)=h(10)+(1−β)   [Equation 6]

As shown in FIGS. 11A-11C, histograms for a-global region, a semi-globalregion, and a local region are generated with the help of block-basedhistograms. The generated histograms may be used as feature informationthat effectively reflect global features and local position informationof an image.

As shown in FIG. 12, six brightness bins and five edge bins may be usedto generate eleven histogram bins regarding global region. Globalfeature information of the image is described by the eleven histogrambins.

A global region is divided into 16 local regions and five edge bins aregenerated at each local region. Consequently, as shown in FIG. 12,eighty histogram bins are generated.

Finally, four semi-global regions of a horizontal direction and foursemi-global regions of a vertical direction are created by accumulatingrelated block edges. Each semi-global region has five edge bins and atotal of forty edge bins are created as shown in FIG. 12.

In case of composite histograms, brightness information for a globalregion is updated at h(0) through h(5) and edge information regarding aglobal region is updated at h(6) through h(10). Edge informationregarding a local region is updated at h(11) through h(90) and edgeinformation regarding a semi-global region is updated at h(91) throughh(130).

Once the sequence of histogram bins is determined, histograms ofsemi-global regions and histograms of local regions describe positioninformation and edge feature information at the same time.

At the moment, the composite histograms are normalized at each region inthe following way.

First, global region histogram bins (global_histogram_value(i)) aredivided by the total number of blocks (global_number_block) that existin the global region. Equation 7 shows the normalization process.$\begin{matrix}{{{{normalize\_ histogram}\lbrack i\rbrack} = \frac{{global\_ histogram}{{\_ value}\lbrack i\rbrack}}{{global\_ number}{\_ block}}},{i = {0\quad\ldots\quad 10}}} & \left\lbrack {{Equation}\quad 7} \right\rbrack\end{matrix}$

Second, regarding an image of each local region, eighty histogram binsof a local region are divided by the total number of blocks(local_global_number_block) in the local region. Equation 8 shows thenormalization process. $\begin{matrix}{{{{normalize\_ histogram}\lbrack i\rbrack} = \frac{{local\_ histogram}{{\_ value}\lbrack i\rbrack}}{{local\_ number}{\_ block}}},{i = {11\quad\ldots\quad 90}}} & \left\lbrack {{Equation}\quad 8} \right\rbrack\end{matrix}$

Finally, regarding an image of each local region, forty bins of asemi-global region are divided by the total number of blocks(semi_global_number_block) in the semi-global region. Equation 9 showsthe normalization process. $\begin{matrix}{{{{normalize\_ histogram}\lbrack i\rbrack} = \frac{{semi\_ global}{\_ histogram}{{\_ value}\lbrack i\rbrack}}{{semi\_ global}{\_ number}{\_ block}}},{i = {91\quad\ldots\quad 130}}} & \left\lbrack {{Equation}\quad 9} \right\rbrack\end{matrix}$

FIG. 12 shows normalize_histogram[i].

Since the method for generating a block-based image histogram inaccordance with the present invention extracts feature information byblocks that include a number of pixels, it is possible to obtain edgeinformation that was not obtained by conventional methods. Also, themethod extracts feature information such as brightness of variousresolutions, color, and edge. Therefore, feature information thatreflects image content is finely acquired and accurate comparison ispossible for similarity measurement and distance measurement.

In addition, since the method employs a linear update method,quantization errors caused at boundaries between bins may besignificantly decreased. Since the method normalizes histogram bins,similar image can be searched regardless of the size of images.

Although representative embodiments of the present invention have beendisclosed for illustrative purpose, those who are skilled in the artwill appreciate that various modifications, additions and substitutionsare possible without departing from the scope and spirit of the presentinvention as defined in the accompanying claims and in equivalentsthereof.

1. A method for generating an edge histogram in an image, which isdivided into one or more local regions, wherein each local region isdivided into a plurality of image blocks, the method comprising:extracting edge information from a plurality of image blocks in a localregion; and generating an edge histogram of the local region from theedge information of the plurality of image blocks in the local region.2. The method of claim 1 wherein the edge information of the imageblocks includes five types of edges, which are a 0 degree edge, a 45degree edge, a 90 degree edge, a 135 degree edge, and a complex valueedge.
 3. The method of claim 2 wherein generating an edge histogramcomprises: determining a type of edge; and increasing the value of ahistogram bin corresponding to the determined type of edge.
 4. Themethod of claim 1 wherein the generated edge histogram has a pluralityof edge histogram bins.
 5. A method for generating an edge histogram inan image, which is divided into one or more local regions, wherein eachlocal region is divided into a plurality of image blocks, each imageblock including a plurality of pixels, the method comprising: extractingedge information from a plurality of image blocks in a local region; andupdating an edge histogram bin based on the edge information to generatethe edge histogram for the local region.
 6. The method of claim 5wherein the edge information includes information of edge types,comprising a 0 degree edge, a 45 degree edge, a 90 degree edge, and a135 degree edge.
 7. The method of claim 6 wherein the information ofedge types further includes information of an edge type that is acomplex value edge.
 8. A method, comprising: extracting edge informationfrom a plurality of image blocks; determining a type of edge from theextracted edge information; and generating an edge histogram using theedge information, including the type of edge, extracted from theplurality of image blocks.
 9. The method of claim 8 wherein the edgeinformation comprises information of edge types, including a 0 degreeedge, a 45 degree edge, a 90 degree edge, and a 135 degree edge.
 10. Themethod of claim 9 wherein the edge information comprises information ofa complex value edge type.
 11. The method of claim 8 wherein thegenerated edge histogram comprises a plurality of edge histogram bins.